Mouse DGEs of SACC-PHHs mono-infected with HBV or coinfected with HBV/HDV_factors donor treatment
Purpose:
To determine the DGE profiles (for mouse genes), relative to uninfected controls, of self-assembling co-cultures of primary human hepatocytes (SACC-PHHs) (co-cultured with 3T3J mouse non-parenchymal cells) mono-infected with HBV or co-infected with HBV/HDV at 8 and 28 days post-infection.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(stringr)
library(ggplot2)
library(reshape2)
library(openxlsx)
library(DESeq2)## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
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## anyDuplicated, append, as.data.frame, cbind, colnames,
## do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, lengths, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unsplit##
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## lowesslibrary(dplyr)
library(RColorBrewer)
library(stringr)
library(genefilter)
library(data.table)##
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library(ggrepel)
library(viridis)## Loading required package: viridisLitesource("http://bioconductor.org/biocLite.R")## Bioconductor version 3.3 (BiocInstaller 1.22.3), ?biocLite for help## A newer version of Bioconductor is available for this version of R,
## ?BiocUpgrade for helpbiocLite("org.Mm.eg.db", suppressUpdates = TRUE)## BioC_mirror: https://bioconductor.org## Using Bioconductor 3.3 (BiocInstaller 1.22.3), R 3.3.3 (2017-03-06).## Installing package(s) 'org.Mm.eg.db'## installing the source package 'org.Mm.eg.db'require(org.Mm.eg.db)## Loading required package: org.Mm.eg.db## Loading required package: AnnotationDbi##
## Attaching package: 'AnnotationDbi'## The following object is masked from 'package:dplyr':
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## select## Function to perform DGE analysis with both donor and treatment set as factors influencing the counts. Since we already sorted out counts into folders containing the ENSEMBL IDs for mouse genes under different infection conditions, we will pull the files from these folders to perform the DGE analysis.
DGE_analysis <- function(sampledirectory) {
a <- basename(Sys.glob(file.path(sampledirectory, "*.txt")))
sample_names <- sub('.txt', '', a)
##Here the donors are renamed based off the Hurel names (i.e. HU___) - RNASeq reads were all named
##using a different ID system.
sampleTable <- data.frame(sampleName = sample_names, sampleFile = a, treatment =
ifelse(grepl("Ctrl", a), "mock", ifelse(grepl("*co|*HDV", a), "coinf", "HBV")), donor =
ifelse(grepl("BD330*", a), "HU1019", ifelse(grepl("BD405*", a), "HU1020",
ifelse(grepl("HU1016*", a), "HU1016", "HU1007"))), time = ifelse(grepl("*D8", a), "d8",
"d28"), replicate = ifelse(grepl("*sample_1|*D8_am|*D8_aa", a), "a",
ifelse(grepl("*sample_2|D28_bm|D28_ba", a), "b",
ifelse(grepl("*sample_3", a), "c", ""))))
dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = sampledirectory, design =
~donor + treatment)
dds
dds@colData
contrast <- c("treatment", levels(sampleTable$treatment))
output_basename <- sprintf("%s-%s_vs_%s_%s_analysis", "mousegenes", contrast[2], contrast[3],
levels(sampleTable$time))
output_basename
dds <- estimateSizeFactors(dds)
dds@colData
dds <- estimateDispersions(dds)
plotDispEsts(dds, main=sprintf("%s Dispersion Estimates", output_basename))
dds <- nbinomWaldTest(dds)
res <- results(dds, contrast=contrast)
res <- res[order(res$padj, -abs(res$log2FoldChange)),]
mcols(res, use.names=TRUE)
##Log-intensity ratios = M values, log-intensity averages = A values
##Red points indicate padj < 0.1.
plotMA(res, alpha=0.1, main=sprintf(output_basename))
attr(res, "filterThreshold")
metadata(res)$alpha
metadata(res)$filterThreshold
plot(metadata(res)$filterNumRej,
type="b", ylab="number of rejections",
xlab="quantiles of filter")
lines(metadata(res)$lo.fit, col="red")
abline(v=metadata(res)$filterTheta)
key = "ENSEMBL"
cols = c("ENTREZID", "SYMBOL", "GENENAME", "ALIAS", "REFSEQ", "ACCNUM")
for (col in cols) {
# Get annotation data for column
annotation_data <- AnnotationDbi::select(org.Mm.eg.db, rownames(res), col, keytype=key)
# Collapse one-to-many relationships
tmp <- aggregate(annotation_data[col], by=annotation_data[key],
# to a list
FUN=function(x)list(x))
# Match on key and append to results
idx <- match(rownames(res), tmp[[key]])
res[[col]] <- tmp[idx,col]
}
output_data <- as.data.frame(res)
LIST_COLS <- sapply(output_data, is.list)
for (COL in colnames(output_data)[LIST_COLS]) {
output_data[COL] <-
sapply(output_data[COL],
function(x)sapply(x, function(y) paste(unlist(y),
collapse=", ") ) )
}
# Save data frame above as tab-separated file
write.table(output_data,
file=file.path("Mouse DGEs_donortreatment", paste(Sys.Date(), "mouse_donor_treatment",
output_basename, "_results.txt", sep='')), quote=FALSE, sep="\t",
row.names=TRUE, col.names=NA)
return(list(dds@colData, head(res)))
}
##For each timepoint, determine the DGE profile when comparing the different treatments groups to one another (i.e. HBV-infected versus control).
##uninfected control cells versus those mono-infected with HBV
DGE_analysis("Mouse d8_ctrlvHBV")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 11 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_Ctrl_D8mousegenes mock HU1019 d8
## BD330_HBV_D8mousegenes HBV HU1019 d8
## BD405A_Ctrl_D8mousegenes mock HU1020 d8
## BD405A_HBV_D8mousegenes HBV HU1020 d8
## Ctrl_D8_sample_1mousegenes mock HU1007 d8 a
## Ctrl_D8_sample_2mousegenes mock HU1007 d8 b
## Ctrl_D8_sample_3mousegenes mock HU1007 d8 c
## HBV_D8_sample_1mousegenes HBV HU1007 d8 a
## HBV_D8_sample_2mousegenes HBV HU1007 d8 b
## HBV_D8_sample_3mousegenes HBV HU1007 d8 c
## HU1016_B_D8mousegenes HBV HU1016 d8
## sizeFactor
## <numeric>
## BD330_Ctrl_D8mousegenes 1.0346642
## BD330_HBV_D8mousegenes 0.7303584
## BD405A_Ctrl_D8mousegenes 0.7721974
## BD405A_HBV_D8mousegenes 0.7067504
## Ctrl_D8_sample_1mousegenes 1.1787427
## Ctrl_D8_sample_2mousegenes 1.3171032
## Ctrl_D8_sample_3mousegenes 1.1331014
## HBV_D8_sample_1mousegenes 1.4396846
## HBV_D8_sample_2mousegenes 0.9738774
## HBV_D8_sample_3mousegenes 1.3937500
## HU1016_B_D8mousegenes 0.8111123
##
## [[2]]
## log2 fold change (MAP): treatment HBV vs mock
## Wald test p-value: treatment HBV vs mock
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000039518 7112.089 0.6768464 0.08554957 7.911745
## ENSMUSG00000035385 1330.663 -0.8361779 0.11319038 -7.387358
## ENSMUSG00000022425 3544.850 0.5343298 0.07548904 7.078244
## ENSMUSG00000022231 1295.233 -0.6244717 0.10742064 -5.813331
## ENSMUSG00000064345 17058.066 0.5268244 0.09128533 5.771184
## ENSMUSG00000023885 3718.135 -0.4011325 0.07877309 -5.092252
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000039518 2.538060e-15 1.841363e-11 ######## ######## ########
## ENSMUSG00000035385 1.497749e-13 5.433085e-10 ######## ######## ########
## ENSMUSG00000022425 1.459929e-12 3.530595e-09 ######## ######## ########
## ENSMUSG00000022231 6.124180e-09 1.110773e-05 ######## ######## ########
## ENSMUSG00000064345 7.871642e-09 1.142175e-05 ######## ######## ########
## ENSMUSG00000023885 3.538346e-07 4.278450e-04 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000039518 ######## ######## ########
## ENSMUSG00000035385 ######## ######## ########
## ENSMUSG00000022425 ######## ######## ########
## ENSMUSG00000022231 ######## ######## ########
## ENSMUSG00000064345 ######## ######## ########
## ENSMUSG00000023885 ######## ######## ########DGE_analysis("Mouse d28_ctrlvHBV")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 11 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_Ctrl_D28mousegenes mock HU1019 d28
## BD330_HBV_D28mousegenes HBV HU1019 d28
## BD405A_Ctrl_D28mousegenes mock HU1020 d28
## BD405A_HBV_D28mousegenes HBV HU1020 d28
## Ctrl_D28_sample_1mousegenes mock HU1007 d28 a
## Ctrl_D28_sample_2mousegenes mock HU1007 d28 b
## Ctrl_D28_sample_3mousegenes mock HU1007 d28 c
## HBV_D28_sample_1mousegenes HBV HU1007 d28 a
## HBV_D28_sample_2mousegenes HBV HU1007 d28 b
## HBV_D28_sample_3mousegenes HBV HU1007 d28 c
## HU1016_B_D28mousegenes HBV HU1016 d28
## sizeFactor
## <numeric>
## BD330_Ctrl_D28mousegenes 1.3557895
## BD330_HBV_D28mousegenes 0.5941100
## BD405A_Ctrl_D28mousegenes 1.1759956
## BD405A_HBV_D28mousegenes 0.9351697
## Ctrl_D28_sample_1mousegenes 0.6763589
## Ctrl_D28_sample_2mousegenes 1.0674628
## Ctrl_D28_sample_3mousegenes 1.1250064
## HBV_D28_sample_1mousegenes 1.1577966
## HBV_D28_sample_2mousegenes 1.3253656
## HBV_D28_sample_3mousegenes 0.8475009
## HU1016_B_D28mousegenes 1.2459546
##
## [[2]]
## log2 fold change (MAP): treatment HBV vs mock
## Wald test p-value: treatment HBV vs mock
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000053279 166.3330 0.7402055 0.14947843 4.951922
## ENSMUSG00000054072 383.0008 0.7242820 0.14384818 5.035045
## ENSMUSG00000074934 727.6756 -0.5567154 0.11792382 -4.720975
## ENSMUSG00000020620 536.7238 0.5707276 0.12588856 4.533594
## ENSMUSG00000064341 27372.8604 0.4464180 0.09853559 4.530525
## ENSMUSG00000017002 360.2129 -0.6184647 0.13912869 -4.445271
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000053279 7.348415e-07 0.003691844 ######## ######## ########
## ENSMUSG00000054072 4.777373e-07 0.003691844 ######## ######## ########
## ENSMUSG00000074934 2.347166e-06 0.007861440 ######## ######## ########
## ENSMUSG00000020620 5.798838e-06 0.011823922 ######## ######## ########
## ENSMUSG00000064341 5.883719e-06 0.011823922 ######## ######## ########
## ENSMUSG00000017002 8.778113e-06 0.014178918 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000053279 ######## ######## ########
## ENSMUSG00000054072 ######## ######## ########
## ENSMUSG00000074934 ######## ######## ########
## ENSMUSG00000020620 ######## ######## ########
## ENSMUSG00000064341 ######## ######## ########
## ENSMUSG00000017002 ######## ######## ##########uninfected control cells versus those co-infected with HBV and HDV
DGE_analysis("Mouse d8_ctrlvcoinf")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 9 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_Ctrl_D8mousegenes mock HU1019 d8
## BD330_HBV_HDV_D8_amousegenes coinf HU1019 d8 a
## BD330_HBV_HDV_D8mousegenes coinf HU1019 d8
## BD405A_Ctrl_D8mousegenes mock HU1020 d8
## BD405A_HBV_HDV_D8mousegenes coinf HU1020 d8
## Ctrl_D8_sample_1mousegenes mock HU1007 d8 a
## Ctrl_D8_sample_2mousegenes mock HU1007 d8 b
## Ctrl_D8_sample_3mousegenes mock HU1007 d8 c
## HU1016_BD_co_D8mousegenes coinf HU1016 d8
## sizeFactor
## <numeric>
## BD330_Ctrl_D8mousegenes 1.1650841
## BD330_HBV_HDV_D8_amousegenes 0.6866507
## BD330_HBV_HDV_D8mousegenes 0.8160634
## BD405A_Ctrl_D8mousegenes 0.8689167
## BD405A_HBV_HDV_D8mousegenes 0.8081074
## Ctrl_D8_sample_1mousegenes 1.3161638
## Ctrl_D8_sample_2mousegenes 1.4582484
## Ctrl_D8_sample_3mousegenes 1.2641291
## HU1016_BD_co_D8mousegenes 0.9779127
##
## [[2]]
## log2 fold change (MAP): treatment coinf vs mock
## Wald test p-value: treatment coinf vs mock
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000050578 291.8666 -2.090830 0.2263143 -9.238612
## ENSMUSG00000021822 1086.3773 -1.633466 0.1754663 -9.309283
## ENSMUSG00000038393 8075.1441 -1.147458 0.1306828 -8.780482
## ENSMUSG00000022330 508.6560 -1.470379 0.2015997 -7.293556
## ENSMUSG00000021831 9416.6190 -1.411263 0.1924699 -7.332386
## ENSMUSG00000038264 1197.6153 -1.222209 0.1679321 -7.277998
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000050578 2.497146e-20 1.629638e-16 ######## ######## ########
## ENSMUSG00000021822 1.286978e-20 1.629638e-16 ######## ######## ########
## ENSMUSG00000038393 1.627758e-18 7.081832e-15 ######## ######## ########
## ENSMUSG00000022330 3.018795e-13 7.370265e-10 ######## ######## ########
## ENSMUSG00000021831 2.260910e-13 7.370265e-10 ######## ######## ########
## ENSMUSG00000038264 3.388109e-13 7.370265e-10 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000050578 ######## ######## ########
## ENSMUSG00000021822 ######## ######## ########
## ENSMUSG00000038393 ######## ######## ########
## ENSMUSG00000022330 ######## ######## ########
## ENSMUSG00000021831 ######## ######## ########
## ENSMUSG00000038264 ######## ######## ########DGE_analysis("Mouse d28_ctrlvcoinf")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 9 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_Ctrl_D28mousegenes mock HU1019 d28
## BD330_HBV_HDV_D28_bmousegenes coinf HU1019 d28 b
## BD330_HBV_HDV_D28mousegenes coinf HU1019 d28
## BD405A_Ctrl_D28mousegenes mock HU1020 d28
## BD405A_HBV_HDV_D28mousegenes coinf HU1020 d28
## Ctrl_D28_sample_1mousegenes mock HU1007 d28 a
## Ctrl_D28_sample_2mousegenes mock HU1007 d28 b
## Ctrl_D28_sample_3mousegenes mock HU1007 d28 c
## HU1016_BD_co_D28mousegenes coinf HU1016 d28
## sizeFactor
## <numeric>
## BD330_Ctrl_D28mousegenes 1.2702580
## BD330_HBV_HDV_D28_bmousegenes 1.2568780
## BD330_HBV_HDV_D28mousegenes 0.9271231
## BD405A_Ctrl_D28mousegenes 1.1054268
## BD405A_HBV_HDV_D28mousegenes 1.0025912
## Ctrl_D28_sample_1mousegenes 0.6297347
## Ctrl_D28_sample_2mousegenes 0.9904098
## Ctrl_D28_sample_3mousegenes 1.0460913
## HU1016_BD_co_D28mousegenes 1.0468278
##
## [[2]]
## log2 fold change (MAP): treatment coinf vs mock
## Wald test p-value: treatment coinf vs mock
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000033491 47.36793 1.0222172 0.1902974 5.371684
## ENSMUSG00000027737 450.84204 -1.0359278 0.2010512 -5.152558
## ENSMUSG00000031673 2396.38523 -0.8112513 0.1670639 -4.855933
## ENSMUSG00000081778 160.56608 -0.7149199 0.1582671 -4.517172
## ENSMUSG00000038305 20.47186 -0.6568132 0.1490415 -4.406914
## ENSMUSG00000005686 1255.98589 0.8288639 0.1952677 4.244756
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000033491 7.800487e-08 0.001085672 ######## ######## ########
## ENSMUSG00000027737 2.569570e-07 0.001788164 ######## ######## ########
## ENSMUSG00000031673 1.198212e-06 0.005558905 ######## ######## ########
## ENSMUSG00000081778 6.267090e-06 0.021806339 ######## ######## ########
## ENSMUSG00000038305 1.048539e-05 0.029187138 ######## ######## ########
## ENSMUSG00000005686 2.188315e-05 0.043509960 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000033491 ######## ######## ########
## ENSMUSG00000027737 ######## ######## ########
## ENSMUSG00000031673 ######## ######## ########
## ENSMUSG00000081778 ######## ######## ########
## ENSMUSG00000038305 ######## ######## ########
## ENSMUSG00000005686 ######## ######## ##########monoinfected cells (HBV only) versus those co-infected with HBV and HDV
DGE_analysis("Mouse d8_coinfvHBV")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 10 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_HBV_D8mousegenes HBV HU1019 d8
## BD330_HBV_HDV_D8_amousegenes coinf HU1019 d8 a
## BD330_HBV_HDV_D8mousegenes coinf HU1019 d8
## BD405A_HBV_D8mousegenes HBV HU1020 d8
## BD405A_HBV_HDV_D8mousegenes coinf HU1020 d8
## HBV_D8_sample_1mousegenes HBV HU1007 d8 a
## HBV_D8_sample_2mousegenes HBV HU1007 d8 b
## HBV_D8_sample_3mousegenes HBV HU1007 d8 c
## HU1016_BD_co_D8mousegenes coinf HU1016 d8
## HU1016_B_D8mousegenes HBV HU1016 d8
## sizeFactor
## <numeric>
## BD330_HBV_D8mousegenes 0.8562166
## BD330_HBV_HDV_D8_amousegenes 0.7165406
## BD330_HBV_HDV_D8mousegenes 0.8547586
## BD405A_HBV_D8mousegenes 0.8338012
## BD405A_HBV_HDV_D8mousegenes 0.8407318
## HBV_D8_sample_1mousegenes 1.6703247
## HBV_D8_sample_2mousegenes 1.1266403
## HBV_D8_sample_3mousegenes 1.6218624
## HU1016_BD_co_D8mousegenes 1.0226829
## HU1016_B_D8mousegenes 0.9515828
##
## [[2]]
## log2 fold change (MAP): treatment coinf vs HBV
## Wald test p-value: treatment coinf vs HBV
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000019102 351.78327 -1.0283347 0.1720169 -5.978104
## ENSMUSG00000028760 290.62080 -1.0129153 0.1675173 -6.046630
## ENSMUSG00000034115 27.07254 0.8958305 0.1497669 5.981499
## ENSMUSG00000038393 7600.29094 -0.7174435 0.1270261 -5.648002
## ENSMUSG00000061353 2945.03987 -0.8394278 0.1500969 -5.592571
## ENSMUSG00000027832 572.94359 -0.8943232 0.1658269 -5.393112
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000019102 2.257500e-09 1.006694e-05 ######## ######## ########
## ENSMUSG00000028760 1.479067e-09 1.006694e-05 ######## ######## ########
## ENSMUSG00000034115 2.210938e-09 1.006694e-05 ######## ######## ########
## ENSMUSG00000038393 1.623230e-08 5.428892e-05 ######## ######## ########
## ENSMUSG00000061353 2.237311e-08 5.986148e-05 ######## ######## ########
## ENSMUSG00000027832 6.924787e-08 1.378168e-04 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000019102 ######## ######## ########
## ENSMUSG00000028760 ######## ######## ########
## ENSMUSG00000034115 ######## ######## ########
## ENSMUSG00000038393 ######## ######## ########
## ENSMUSG00000061353 ######## ######## ########
## ENSMUSG00000027832 ######## ######## ########DGE_analysis("Mouse d28_coinfvHBV")## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [[1]]
## DataFrame with 10 rows and 5 columns
## treatment donor time replicate
## <factor> <factor> <factor> <factor>
## BD330_HBV_D28mousegenes HBV HU1019 d28
## BD330_HBV_HDV_D28_bmousegenes coinf HU1019 d28 b
## BD330_HBV_HDV_D28mousegenes coinf HU1019 d28
## BD405A_HBV_D28mousegenes HBV HU1020 d28
## BD405A_HBV_HDV_D28mousegenes coinf HU1020 d28
## HBV_D28_sample_1mousegenes HBV HU1007 d28 a
## HBV_D28_sample_2mousegenes HBV HU1007 d28 b
## HBV_D28_sample_3mousegenes HBV HU1007 d28 c
## HU1016_BD_co_D28mousegenes coinf HU1016 d28
## HU1016_B_D28mousegenes HBV HU1016 d28
## sizeFactor
## <numeric>
## BD330_HBV_D28mousegenes 0.5817231
## BD330_HBV_HDV_D28_bmousegenes 1.3155398
## BD330_HBV_HDV_D28mousegenes 0.9626580
## BD405A_HBV_D28mousegenes 0.9143691
## BD405A_HBV_HDV_D28mousegenes 1.0495034
## HBV_D28_sample_1mousegenes 1.1233937
## HBV_D28_sample_2mousegenes 1.2779097
## HBV_D28_sample_3mousegenes 0.8260627
## HU1016_BD_co_D28mousegenes 1.0925807
## HU1016_B_D28mousegenes 1.2239818
##
## [[2]]
## log2 fold change (MAP): treatment coinf vs HBV
## Wald test p-value: treatment coinf vs HBV
## DataFrame with 6 rows and 12 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000056427 1093.7615 -1.0388053 0.1693633 -6.133591
## ENSMUSG00000006014 82.5130 -1.1933384 0.2066009 -5.776057
## ENSMUSG00000018830 128.1758 -1.1486217 0.2104613 -5.457638
## ENSMUSG00000050936 183.9195 -1.0065217 0.2035346 -4.945213
## ENSMUSG00000052565 120.4718 -1.0166065 0.2101651 -4.837181
## ENSMUSG00000032690 432.4550 -0.9004502 0.1850722 -4.865399
## pvalue padj ENTREZID SYMBOL GENENAME
## <numeric> <numeric> <list> <list> <list>
## ENSMUSG00000056427 8.591721e-10 1.241590e-05 ######## ######## ########
## ENSMUSG00000006014 7.647158e-09 5.525454e-05 ######## ######## ########
## ENSMUSG00000018830 4.825113e-08 2.324257e-04 ######## ######## ########
## ENSMUSG00000050936 7.606073e-07 2.747884e-03 ######## ######## ########
## ENSMUSG00000052565 1.316937e-06 3.171844e-03 ######## ######## ########
## ENSMUSG00000032690 1.142263e-06 3.171844e-03 ######## ######## ########
## ALIAS REFSEQ ACCNUM
## <list> <list> <list>
## ENSMUSG00000056427 ######## ######## ########
## ENSMUSG00000006014 ######## ######## ########
## ENSMUSG00000018830 ######## ######## ########
## ENSMUSG00000050936 ######## ######## ########
## ENSMUSG00000052565 ######## ######## ########
## ENSMUSG00000032690 ######## ######## ########Session Info
sessionInfo()## R version 3.3.3 (2017-03-06)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Mm.eg.db_3.3.0 AnnotationDbi_1.34.4
## [3] BiocInstaller_1.22.3 viridis_0.4.0
## [5] viridisLite_0.2.0 ggrepel_0.6.5
## [7] data.table_1.10.0 genefilter_1.54.2
## [9] RColorBrewer_1.1-2 gplots_3.0.1
## [11] DESeq2_1.12.4 SummarizedExperiment_1.2.3
## [13] Biobase_2.32.0 GenomicRanges_1.24.3
## [15] GenomeInfoDb_1.8.7 IRanges_2.6.1
## [17] S4Vectors_0.10.3 BiocGenerics_0.18.0
## [19] openxlsx_4.0.17 reshape2_1.4.2
## [21] ggplot2_2.2.1 stringr_1.2.0
## [23] dplyr_0.7.3
##
## loaded via a namespace (and not attached):
## [1] splines_3.3.3 gtools_3.5.0 Formula_1.2-1
## [4] assertthat_0.2.0 latticeExtra_0.6-28 yaml_2.1.14
## [7] RSQLite_1.1-2 backports_1.0.5 lattice_0.20-35
## [10] glue_1.1.1 digest_0.6.12 XVector_0.12.1
## [13] checkmate_1.8.2 colorspace_1.3-2 htmltools_0.3.5
## [16] Matrix_1.2-8 plyr_1.8.4 XML_3.98-1.9
## [19] pkgconfig_2.0.1 zlibbioc_1.18.0 xtable_1.8-2
## [22] scales_0.4.1 gdata_2.17.0 BiocParallel_1.6.6
## [25] htmlTable_1.9 tibble_1.3.3 annotate_1.50.1
## [28] nnet_7.3-12 lazyeval_0.2.0 survival_2.41-3
## [31] magrittr_1.5 memoise_1.0.0 evaluate_0.10
## [34] foreign_0.8-67 tools_3.3.3 munsell_0.4.3
## [37] locfit_1.5-9.1 cluster_2.0.6 bindrcpp_0.2
## [40] caTools_1.17.1 rlang_0.1.2 grid_3.3.3
## [43] RCurl_1.95-4.8 htmlwidgets_0.9 bitops_1.0-6
## [46] base64enc_0.1-3 rmarkdown_1.4 gtable_0.2.0
## [49] DBI_0.6-1 R6_2.2.0 gridExtra_2.2.1
## [52] knitr_1.16 bindr_0.1 Hmisc_4.0-2
## [55] rprojroot_1.2 KernSmooth_2.23-15 stringi_1.1.5
## [58] Rcpp_0.12.10 geneplotter_1.50.0 rpart_4.1-10
## [61] acepack_1.4.1